文献类型：期刊文献

英文题名：A Survey of Recommender Systems Based on Deep Learning

作者：Mu, Ruihui[1,2]

第一作者：Mu, Ruihui;穆瑞辉

通讯作者：Mu, RH[1];Mu, RH[2]

机构：[1]Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China;[2]Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453003, Peoples R China

第一机构：Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China

通讯机构：[1]corresponding author), Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China;[2]corresponding author), Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453003, Peoples R China.|[1107118]新乡学院计算机与信息工程学院;[11071]新乡学院;

年份：2018

卷号：6

起止页码：69009-69022

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20184706088462);Scopus(收录号：2-s2.0-85056521754);WOS:【SCI-EXPANDED(收录号:WOS:000452755700001)】；

基金：This work was supported in part by the National Key Research and Development Plan Key Projects of China under Grant 2017YFC0405800, in part by the National Natural Science Foundation of China under Grant 60971088, Grant 60571048, Grant 61432008, and Grant 61375121, and in part by the Natural Science Foundation of the Colleges and Universities in Jiangsu Province of China under Grant 17KJB520022.

语种：英文

外文关键词：Deep learning; recommender systems; deep learning-based recommender systems; machine learning; terminology

摘要：In recent years, deep learning's revolutionary advances in speech recognition, image analysis, and natural language processing have gained significant attention. Deep learning technology has become a hotspot research field in the artificial intelligence and has been applied into recommender system. In contrast to traditional recommendation models, deep learning is able to effectively capture the non-linear and non-trivial user-item relationships and enables the codification of more complex abstractions as data representations in the higher layers. In this paper, we provide a comprehensive review of the related research contents of deep learning-based recommender systems. First, we introduce the basic terminologies and the background concepts of recommender systems and deep learning technology. Second, we describe the main current research on deep learning-based recommender systems. Third, we provide the possible research directions of deep learning-based recommender systems in the future. Finally, concludes this paper.